Combining numerous data sources has always been a high priority topic, but with the advent of new AI-based learning algorithms, it has become critical to combine the complementary capabilities of distinct data sources for effective diagnosis, treatment, prognosis, and planning in a variety of medical applications. With the onset of COVID-19 pandemic, patient pre-screening from passively recorded audios has become an active area of research. Therefore, a bunch of unimodal and multimodal COVID-19 audio dataset have been presented [1]}, [2]}, [3]}, [4]}. The ultimate goal in this research is to provide COVID-19 pre-screening mobile or tiny devices. Figure REF  shows the highlevel overview of the evaluation of TinyM\(^2\) Net in terms of COVID-19 detection.
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